% Key papers for Group symmetry equivariant networks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{icml2019_gauge_37390,
  title = {{Gauge Equivariant Convolutional Networks and the Icosahedral CNN}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3872},
}

@inproceedings{icml2017_equivariance_36179,
  title = {{Equivariance Through Parameter-Sharing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/686},
}

@inproceedings{neurips2018_group_57190,
  title = {{Group Equivariant Capsule Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11844},
}

@inproceedings{icml2019_equivariant_37338,
  title = {{Equivariant Transformer Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/3908},
}

@inproceedings{icml2021_lietransformer_39563,
  title = {{LieTransformer: Equivariant Self-Attention for Lie Groups}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8407},
}

@inproceedings{iclr2021_meta_29930,
  title = {{Meta-learning Symmetries by Reparameterization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2607},
}

@inproceedings{neurips2021_automatic_62059,
  title = {{Automatic Symmetry Discovery with Lie Algebra Convolutional Network}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27131},
}

@inproceedings{iclr2020_co_29749,
  title = {{Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=r1g6ogrtDr},
}

@inproceedings{cvpr2022_enabling_18753,
  title = {{Enabling Equivariance for Arbitrary Lie Groups}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{icml2020_pdo_38607,
  title = {{PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6749},
}

@inproceedings{cvpr2022_nested_19421,
  title = {{Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{iclr2020_building_29290,
  title = {{Building Deep Equivariant Capsule Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=BJgNJgSFPS},
}

@inproceedings{iclr2023_the_32377,
  title = {{The Lie Derivative for Measuring Learned Equivariance}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11044},
}

@inproceedings{neurips2022_relaxing_63994,
  title = {{Relaxing Equivariance Constraints with Non-stationary Continuous Filters}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53326},
}

@inproceedings{icml2021_equivariant_39302,
  title = {{Equivariant Networks for Pixelized Spheres}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10393},
}

@inproceedings{iclr2024_enabling_35187,
  title = {{Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17886},
}

@inproceedings{neurips2021_reverse_61548,
  title = {{Reverse-Complement Equivariant Networks for DNA Sequences}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28092},
}

@inproceedings{neurips2023_a_66734,
  title = {{A General Framework for Equivariant Neural Networks on Reductive Lie Groups}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72898},
}

@inproceedings{neurips2023_normalization_67786,
  title = {{Normalization-Equivariant Neural Networks with Application to Image Denoising}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71971},
}

@inproceedings{neurips2025_lorentz_74969,
  title = {{Lorentz Local Canonicalization: How to make any Network Lorentz-Equivariant}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=2xeYuWd7WZ},
}

@inproceedings{aaai2024_continuous_5492,
  title = {{Continuous Rotation Group Equivariant Network Inspired by Neural Population Coding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29027},
}

@inproceedings{cvpr2024_affine_22740,
  title = {{Affine Equivariant Networks Based on Differential Invariants}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30417},
}

@inproceedings{iclr2025_lie_80943,
  title = {{Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30825},
}

@inproceedings{iclr2024_a_33703,
  title = {{A Characterization Theorem for Equivariant Networks with Point-wise Activations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19372},
}

@inproceedings{iclr2024_lie_35276,
  title = {{Lie Group Decompositions for Equivariant Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17797},
}

@inproceedings{icml2025_flopping_46595,
  title = {{Flopping for FLOPs: Leveraging Equivariance for Computational Efficiency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45974},
}

@inproceedings{icml2025_glgenn_46772,
  title = {{GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45802},
}

@inproceedings{icml2025_equivariant_48580,
  title = {{Equivariant Neural Tangent Kernels}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44041},
}

@inproceedings{icml2026_normalization_50430,
  title = {{Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=CKhydx7QgH},
}

@inproceedings{icml2026_approximate_50577,
  title = {{Approximate Equivariance via Projection-Based Regularisation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Dly6gCtE4V},
}

@inproceedings{icml2026_platonic_51185,
  title = {{Platonic Transformers: A Solid Choice For Equivariance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=JTu5Mawn86},
}

@inproceedings{icml2026_a_51365,
  title = {{A Hypertoroidal Covering for Perfect Color Equivariance}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=LQNrpOXqoB},
}

@inproceedings{neurips2024_learning_70406,
  title = {{Learning symmetries via weight-sharing with doubly stochastic tensors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96699},
}

@inproceedings{neurips2024_monomial_74096,
  title = {{Monomial Matrix Group Equivariant Neural Functional Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93437},
}

@inproceedings{neurips2025_learning_75320,
  title = {{Learning Chern Numbers of Multiband Topological Insulators with Gauge Equivariant Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=6pjzFIyFBo},
}

@inproceedings{iclr2025_revisiting_80783,
  title = {{REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30985},
}

@inproceedings{iclr2026_a_84932,
  title = {{A Single Architecture for Representing Invariance Under Any Space Group}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=8LZrXh9hhL},
}

@inproceedings{iclr2026_reducing_87619,
  title = {{Reducing Symmetry Increase in Equivariant Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=dUgq4bLY4X},
}